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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | ENHANCING QUALITY CONTROL: A DEEP LEARNING APPROACH FOR VISUAL INSPECTION |
| Authors Name: | S.MANOJ KUMAR , M.MARKANDAYAN , P. YUVARAJ , B.ASWINKANTH |
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IJNRD_220011
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| Published Paper Id: | IJNRDTH00144 |
| Published In: | Volume 9 Issue 5, May-2024 |
| DOI: | |
| Abstract: | The production and distribution of bottled water have witnessed exponential growth globally, driven by factors such as convenience, health consciousness, and urbanization. With this surge in demand, ensuring the quality and integrity of bottled water products has become a top priority for manufacturers. Central to this endeavour is the need for effective inspection methods to detect and mitigate defects that may compromise product safety and consumer satisfaction. With the proliferation of bottled water consumption, ensuring the quality and safety of water bottles has become increasingly vital. Visual inspection methods provide a non-invasive and efficient means of identifying defects in water bottles during manufacturing processes. In this study, we propose a novel approach for the visual inspection of water bottles using YOLO, a deep learning architecture known for its effectiveness in image classification tasks. The proposed system employs YOLO algorithm to analyse images of water bottles captured by cameras installed along the production line. By leveraging the hierarchical feature representations learned by YOLO algorithm, our method aims to accurately classify water bottles into categories such as "defective" or "acceptable" based on the presence of defects such as scratches, dents, or impurities. We also explore strategies for optimizing model hyperparameters and training parameters to improve classification performance. |
| Keywords: | Quality assurance , Deep learning , Defect detection , visual inspection method. |
| Cite Article: | "ENHANCING QUALITY CONTROL: A DEEP LEARNING APPROACH FOR VISUAL INSPECTION", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 5, page no.340-402, May-2024, Available :http://www.ijnrd.org/papers/IJNRDTH00144.pdf |
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2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRDTH00144 Registration ID: 220011 Published In: Volume 9 Issue 5, May-2024 DOI (Digital Object Identifier): Page No: 340-402 Country: madurai, tamil nadu, India Research Area: Computer Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRDTH00144 Published Paper PDF: https://www.ijnrd.org/papers/IJNRDTH00144 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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